


<!DOCTYPE html>
<!--[if IE 8]><html class="no-js lt-ie9" lang="en" > <![endif]-->
<!--[if gt IE 8]><!--> <html class="no-js" lang="en" > <!--<![endif]-->
<head>
  <meta charset="utf-8">
  
  <meta name="viewport" content="width=device-width, initial-scale=1.0">
  
  <title>torch.nn.modules.sparse &mdash; PyTorch master documentation</title>
  

  
  
  
  
    <link rel="canonical" href="https://pytorch.org/docs/stable/_modules/torch/nn/modules/sparse.html"/>
  

  

  
  
    

  

  <link rel="stylesheet" href="../../../../_static/css/theme.css" type="text/css" />
  <!-- <link rel="stylesheet" href="../../../../_static/pygments.css" type="text/css" /> -->
  <link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/katex@0.10.0-beta/dist/katex.min.css" type="text/css" />
  <link rel="stylesheet" href="../../../../_static/css/jit.css" type="text/css" />
  <link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/katex@0.11.1/dist/katex.min.css" type="text/css" />
  <link rel="stylesheet" href="../../../../_static/katex-math.css" type="text/css" />
    <link rel="index" title="Index" href="../../../../genindex.html" />
    <link rel="search" title="Search" href="../../../../search.html" /> 

  
  <script src="../../../../_static/js/modernizr.min.js"></script>

  <!-- Preload the theme fonts -->

<link rel="preload" href="../../../../_static/fonts/FreightSans/freight-sans-book.woff2" as="font" type="font/woff2" crossorigin="anonymous">
<link rel="preload" href="../../../../_static/fonts/FreightSans/freight-sans-medium.woff2" as="font" type="font/woff2" crossorigin="anonymous">
<link rel="preload" href="../../../../_static/fonts/IBMPlexMono/IBMPlexMono-Medium.woff2" as="font" type="font/woff2" crossorigin="anonymous">
<link rel="preload" href="../../../../_static/fonts/FreightSans/freight-sans-bold.woff2" as="font" type="font/woff2" crossorigin="anonymous">
<link rel="preload" href="../../../../_static/fonts/FreightSans/freight-sans-medium-italic.woff2" as="font" type="font/woff2" crossorigin="anonymous">
<link rel="preload" href="../../../../_static/fonts/IBMPlexMono/IBMPlexMono-SemiBold.woff2" as="font" type="font/woff2" crossorigin="anonymous">

<!-- Preload the katex fonts -->

<link rel="preload" href="https://cdn.jsdelivr.net/npm/katex@0.10.0/dist/fonts/KaTeX_Math-Italic.woff2" as="font" type="font/woff2" crossorigin="anonymous">
<link rel="preload" href="https://cdn.jsdelivr.net/npm/katex@0.10.0/dist/fonts/KaTeX_Main-Regular.woff2" as="font" type="font/woff2" crossorigin="anonymous">
<link rel="preload" href="https://cdn.jsdelivr.net/npm/katex@0.10.0/dist/fonts/KaTeX_Main-Bold.woff2" as="font" type="font/woff2" crossorigin="anonymous">
<link rel="preload" href="https://cdn.jsdelivr.net/npm/katex@0.10.0/dist/fonts/KaTeX_Size1-Regular.woff2" as="font" type="font/woff2" crossorigin="anonymous">
<link rel="preload" href="https://cdn.jsdelivr.net/npm/katex@0.10.0/dist/fonts/KaTeX_Size4-Regular.woff2" as="font" type="font/woff2" crossorigin="anonymous">
<link rel="preload" href="https://cdn.jsdelivr.net/npm/katex@0.10.0/dist/fonts/KaTeX_Size2-Regular.woff2" as="font" type="font/woff2" crossorigin="anonymous">
<link rel="preload" href="https://cdn.jsdelivr.net/npm/katex@0.10.0/dist/fonts/KaTeX_Size3-Regular.woff2" as="font" type="font/woff2" crossorigin="anonymous">
<link rel="preload" href="https://cdn.jsdelivr.net/npm/katex@0.10.0/dist/fonts/KaTeX_Caligraphic-Regular.woff2" as="font" type="font/woff2" crossorigin="anonymous">
</head>

<div class="container-fluid header-holder tutorials-header" id="header-holder">
  <div class="container">
    <div class="header-container">
      <a class="header-logo" href="https://pytorch.org/" aria-label="PyTorch"></a>

      <div class="main-menu">
        <ul>
          <li>
            <a href="https://pytorch.org/get-started">Get Started</a>
          </li>

          <li>
            <div class="ecosystem-dropdown">
              <a id="dropdownMenuButton" data-toggle="ecosystem-dropdown">
                Ecosystem
              </a>
              <div class="ecosystem-dropdown-menu">
                <a class="nav-dropdown-item" href="https://pytorch.org/hub"">
                  <span class=dropdown-title>Models (Beta)</span>
                  <p>Discover, publish, and reuse pre-trained models</p>
                </a>
                <a class="nav-dropdown-item" href="https://pytorch.org/ecosystem">
                  <span class=dropdown-title>Tools & Libraries</span>
                  <p>Explore the ecosystem of tools and libraries</p>
                </a>
              </div>
            </div>
          </li>

          <li>
            <a href="https://pytorch.org/mobile">Mobile</a>
          </li>

          <li>
            <a href="https://pytorch.org/blog/">Blog</a>
          </li>

          <li>
            <a href="https://pytorch.org/tutorials">Tutorials</a>
          </li>

          <li class="active">
            <a href="https://pytorch.org/docs/stable/index.html">Docs</a>
          </li>

          <li>
            <div class="resources-dropdown">
              <a id="resourcesDropdownButton" data-toggle="resources-dropdown">
                Resources
              </a>
              <div class="resources-dropdown-menu">
                <a class="nav-dropdown-item" href="https://pytorch.org/resources"">
                  <span class=dropdown-title>Developer Resources</span>
                  <p>Find resources and get questions answered</p>
                </a>
                <a class="nav-dropdown-item" href="https://pytorch.org/features">
                  <span class=dropdown-title>About</span>
                  <p>Learn about PyTorch’s features and capabilities</p>
                </a>
              </div>
            </div>
          </li>

          <li>
            <a href="https://github.com/pytorch/pytorch">Github</a>
          </li>
        </ul>
      </div>

      <a class="main-menu-open-button" href="#" data-behavior="open-mobile-menu"></a>
    </div>

  </div>
</div>


<body class="pytorch-body">

   

    

    <div class="table-of-contents-link-wrapper">
      <span>Table of Contents</span>
      <a href="#" class="toggle-table-of-contents" data-behavior="toggle-table-of-contents"></a>
    </div>

    <nav data-toggle="wy-nav-shift" class="pytorch-left-menu" id="pytorch-left-menu">
      <div class="pytorch-side-scroll">
        <div class="pytorch-menu pytorch-menu-vertical" data-spy="affix" role="navigation" aria-label="main navigation">
          <div class="pytorch-left-menu-search">
            

            
              
              
                <div class="version">
                  master (1.5.0 )
                </div>
              
            

            


  


<div role="search">
  <form id="rtd-search-form" class="wy-form" action="../../../../search.html" method="get">
    <input type="text" name="q" placeholder="Search Docs" />
    <input type="hidden" name="check_keywords" value="yes" />
    <input type="hidden" name="area" value="default" />
  </form>
</div>

            
          </div>

          
<div>
  <a style="color:#F05732" href="https://pytorch.org/docs/stable/_modules/torch/nn/modules/sparse.html">
    You are viewing unstable developer preview docs.
    Click here to view docs for latest stable release.
  </a>
</div>

            
            
              
            
            
              <p class="caption"><span class="caption-text">Notes</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../../notes/amp_examples.html">Automatic Mixed Precision examples</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../../notes/autograd.html">Autograd mechanics</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../../notes/broadcasting.html">Broadcasting semantics</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../../notes/cpu_threading_torchscript_inference.html">CPU threading and TorchScript inference</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../../notes/cuda.html">CUDA semantics</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../../notes/ddp.html">Distributed Data Parallel</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../../notes/extending.html">Extending PyTorch</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../../notes/faq.html">Frequently Asked Questions</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../../notes/large_scale_deployments.html">Features for large-scale deployments</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../../notes/multiprocessing.html">Multiprocessing best practices</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../../notes/randomness.html">Reproducibility</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../../notes/serialization.html">Serialization semantics</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../../notes/windows.html">Windows FAQ</a></li>
</ul>
<p class="caption"><span class="caption-text">Language Bindings</span></p>
<ul>
<li class="toctree-l1"><a class="reference external" href="https://pytorch.org/cppdocs/">C++ API</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../../packages.html">Javadoc</a></li>
</ul>
<p class="caption"><span class="caption-text">Python API</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../../torch.html">torch</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../../nn.html">torch.nn</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../../nn.functional.html">torch.nn.functional</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../../tensors.html">torch.Tensor</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../../tensor_attributes.html">Tensor Attributes</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../../tensor_view.html">Tensor Views</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../../autograd.html">torch.autograd</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../../cuda.html">torch.cuda</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../../amp.html">torch.cuda.amp</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../../distributed.html">torch.distributed</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../../distributions.html">torch.distributions</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../../hub.html">torch.hub</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../../jit.html">torch.jit</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../../nn.init.html">torch.nn.init</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../../onnx.html">torch.onnx</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../../optim.html">torch.optim</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../../quantization.html">Quantization</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../../rpc/index.html">Distributed RPC Framework</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../../random.html">torch.random</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../../sparse.html">torch.sparse</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../../storage.html">torch.Storage</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../../bottleneck.html">torch.utils.bottleneck</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../../checkpoint.html">torch.utils.checkpoint</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../../cpp_extension.html">torch.utils.cpp_extension</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../../data.html">torch.utils.data</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../../dlpack.html">torch.utils.dlpack</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../../model_zoo.html">torch.utils.model_zoo</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../../tensorboard.html">torch.utils.tensorboard</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../../type_info.html">Type Info</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../../named_tensor.html">Named Tensors</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../../name_inference.html">Named Tensors operator coverage</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../../__config__.html">torch.__config__</a></li>
</ul>
<p class="caption"><span class="caption-text">Libraries</span></p>
<ul>
<li class="toctree-l1"><a class="reference external" href="https://pytorch.org/audio">torchaudio</a></li>
<li class="toctree-l1"><a class="reference external" href="https://pytorch.org/text">torchtext</a></li>
<li class="toctree-l1"><a class="reference external" href="https://pytorch.org/elastic/">TorchElastic</a></li>
<li class="toctree-l1"><a class="reference external" href="https://pytorch.org/serve">TorchServe</a></li>
<li class="toctree-l1"><a class="reference external" href="http://pytorch.org/xla/">PyTorch on XLA Devices</a></li>
</ul>
<p class="caption"><span class="caption-text">Community</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../../community/contribution_guide.html">PyTorch Contribution Guide</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../../community/governance.html">PyTorch Governance</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../../community/persons_of_interest.html">PyTorch Governance | Persons of Interest</a></li>
</ul>

            
          

        </div>
      </div>
    </nav>

    <div class="pytorch-container">
      <div class="pytorch-page-level-bar" id="pytorch-page-level-bar">
        <div class="pytorch-breadcrumbs-wrapper">
          















<div role="navigation" aria-label="breadcrumbs navigation">

  <ul class="pytorch-breadcrumbs">
    
      <li>
        <a href="../../../../index.html">
          
            Docs
          
        </a> &gt;
      </li>

        
          <li><a href="../../../index.html">Module code</a> &gt;</li>
        
          <li><a href="../../../torch.html">torch</a> &gt;</li>
        
      <li>torch.nn.modules.sparse</li>
    
    
      <li class="pytorch-breadcrumbs-aside">
        
      </li>
    
  </ul>

  
</div>
        </div>

        <div class="pytorch-shortcuts-wrapper" id="pytorch-shortcuts-wrapper">
          Shortcuts
        </div>
      </div>

      <section data-toggle="wy-nav-shift" id="pytorch-content-wrap" class="pytorch-content-wrap">
        <div class="pytorch-content-left">

        
          
          <div class="rst-content">
          
            <div role="main" class="main-content" itemscope="itemscope" itemtype="http://schema.org/Article">
             <article itemprop="articleBody" id="pytorch-article" class="pytorch-article">
              
  <h1>Source code for torch.nn.modules.sparse</h1><div class="highlight"><pre>
<span></span><span class="kn">import</span> <span class="nn">torch</span>
<span class="kn">from</span> <span class="nn">torch.nn.parameter</span> <span class="kn">import</span> <span class="n">Parameter</span>

<span class="kn">from</span> <span class="nn">.module</span> <span class="kn">import</span> <span class="n">Module</span>
<span class="kn">from</span> <span class="nn">..</span> <span class="kn">import</span> <span class="n">functional</span> <span class="k">as</span> <span class="n">F</span>
<span class="kn">from</span> <span class="nn">..</span> <span class="kn">import</span> <span class="n">init</span>


<div class="viewcode-block" id="Embedding"><a class="viewcode-back" href="../../../../nn.html#torch.nn.Embedding">[docs]</a><span class="k">class</span> <span class="nc">Embedding</span><span class="p">(</span><span class="n">Module</span><span class="p">):</span>
    <span class="sa">r</span><span class="sd">&quot;&quot;&quot;A simple lookup table that stores embeddings of a fixed dictionary and size.</span>

<span class="sd">    This module is often used to store word embeddings and retrieve them using indices.</span>
<span class="sd">    The input to the module is a list of indices, and the output is the corresponding</span>
<span class="sd">    word embeddings.</span>

<span class="sd">    Args:</span>
<span class="sd">        num_embeddings (int): size of the dictionary of embeddings</span>
<span class="sd">        embedding_dim (int): the size of each embedding vector</span>
<span class="sd">        padding_idx (int, optional): If given, pads the output with the embedding vector at :attr:`padding_idx`</span>
<span class="sd">                                         (initialized to zeros) whenever it encounters the index.</span>
<span class="sd">        max_norm (float, optional): If given, each embedding vector with norm larger than :attr:`max_norm`</span>
<span class="sd">                                    is renormalized to have norm :attr:`max_norm`.</span>
<span class="sd">        norm_type (float, optional): The p of the p-norm to compute for the :attr:`max_norm` option. Default ``2``.</span>
<span class="sd">        scale_grad_by_freq (boolean, optional): If given, this will scale gradients by the inverse of frequency of</span>
<span class="sd">                                                the words in the mini-batch. Default ``False``.</span>
<span class="sd">        sparse (bool, optional): If ``True``, gradient w.r.t. :attr:`weight` matrix will be a sparse tensor.</span>
<span class="sd">                                 See Notes for more details regarding sparse gradients.</span>

<span class="sd">    Attributes:</span>
<span class="sd">        weight (Tensor): the learnable weights of the module of shape (num_embeddings, embedding_dim)</span>
<span class="sd">                         initialized from :math:`\mathcal{N}(0, 1)`</span>

<span class="sd">    Shape:</span>
<span class="sd">        - Input: :math:`(*)`, LongTensor of arbitrary shape containing the indices to extract</span>
<span class="sd">        - Output: :math:`(*, H)`, where `*` is the input shape and :math:`H=\text{embedding\_dim}`</span>

<span class="sd">    .. note::</span>
<span class="sd">        Keep in mind that only a limited number of optimizers support</span>
<span class="sd">        sparse gradients: currently it&#39;s :class:`optim.SGD` (`CUDA` and `CPU`),</span>
<span class="sd">        :class:`optim.SparseAdam` (`CUDA` and `CPU`) and :class:`optim.Adagrad` (`CPU`)</span>

<span class="sd">    .. note::</span>
<span class="sd">        With :attr:`padding_idx` set, the embedding vector at</span>
<span class="sd">        :attr:`padding_idx` is initialized to all zeros. However, note that this</span>
<span class="sd">        vector can be modified afterwards, e.g., using a customized</span>
<span class="sd">        initialization method, and thus changing the vector used to pad the</span>
<span class="sd">        output. The gradient for this vector from :class:`~torch.nn.Embedding`</span>
<span class="sd">        is always zero.</span>

<span class="sd">    Examples::</span>

<span class="sd">        &gt;&gt;&gt; # an Embedding module containing 10 tensors of size 3</span>
<span class="sd">        &gt;&gt;&gt; embedding = nn.Embedding(10, 3)</span>
<span class="sd">        &gt;&gt;&gt; # a batch of 2 samples of 4 indices each</span>
<span class="sd">        &gt;&gt;&gt; input = torch.LongTensor([[1,2,4,5],[4,3,2,9]])</span>
<span class="sd">        &gt;&gt;&gt; embedding(input)</span>
<span class="sd">        tensor([[[-0.0251, -1.6902,  0.7172],</span>
<span class="sd">                 [-0.6431,  0.0748,  0.6969],</span>
<span class="sd">                 [ 1.4970,  1.3448, -0.9685],</span>
<span class="sd">                 [-0.3677, -2.7265, -0.1685]],</span>

<span class="sd">                [[ 1.4970,  1.3448, -0.9685],</span>
<span class="sd">                 [ 0.4362, -0.4004,  0.9400],</span>
<span class="sd">                 [-0.6431,  0.0748,  0.6969],</span>
<span class="sd">                 [ 0.9124, -2.3616,  1.1151]]])</span>


<span class="sd">        &gt;&gt;&gt; # example with padding_idx</span>
<span class="sd">        &gt;&gt;&gt; embedding = nn.Embedding(10, 3, padding_idx=0)</span>
<span class="sd">        &gt;&gt;&gt; input = torch.LongTensor([[0,2,0,5]])</span>
<span class="sd">        &gt;&gt;&gt; embedding(input)</span>
<span class="sd">        tensor([[[ 0.0000,  0.0000,  0.0000],</span>
<span class="sd">                 [ 0.1535, -2.0309,  0.9315],</span>
<span class="sd">                 [ 0.0000,  0.0000,  0.0000],</span>
<span class="sd">                 [-0.1655,  0.9897,  0.0635]]])</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="n">__constants__</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;num_embeddings&#39;</span><span class="p">,</span> <span class="s1">&#39;embedding_dim&#39;</span><span class="p">,</span> <span class="s1">&#39;padding_idx&#39;</span><span class="p">,</span> <span class="s1">&#39;max_norm&#39;</span><span class="p">,</span>
                     <span class="s1">&#39;norm_type&#39;</span><span class="p">,</span> <span class="s1">&#39;scale_grad_by_freq&#39;</span><span class="p">,</span> <span class="s1">&#39;sparse&#39;</span><span class="p">]</span>

    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">num_embeddings</span><span class="p">,</span> <span class="n">embedding_dim</span><span class="p">,</span> <span class="n">padding_idx</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
                 <span class="n">max_norm</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">norm_type</span><span class="o">=</span><span class="mf">2.</span><span class="p">,</span> <span class="n">scale_grad_by_freq</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
                 <span class="n">sparse</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">_weight</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
        <span class="nb">super</span><span class="p">(</span><span class="n">Embedding</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">num_embeddings</span> <span class="o">=</span> <span class="n">num_embeddings</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">embedding_dim</span> <span class="o">=</span> <span class="n">embedding_dim</span>
        <span class="k">if</span> <span class="n">padding_idx</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
            <span class="k">if</span> <span class="n">padding_idx</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
                <span class="k">assert</span> <span class="n">padding_idx</span> <span class="o">&lt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_embeddings</span><span class="p">,</span> <span class="s1">&#39;Padding_idx must be within num_embeddings&#39;</span>
            <span class="k">elif</span> <span class="n">padding_idx</span> <span class="o">&lt;</span> <span class="mi">0</span><span class="p">:</span>
                <span class="k">assert</span> <span class="n">padding_idx</span> <span class="o">&gt;=</span> <span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">num_embeddings</span><span class="p">,</span> <span class="s1">&#39;Padding_idx must be within num_embeddings&#39;</span>
                <span class="n">padding_idx</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_embeddings</span> <span class="o">+</span> <span class="n">padding_idx</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">padding_idx</span> <span class="o">=</span> <span class="n">padding_idx</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">max_norm</span> <span class="o">=</span> <span class="n">max_norm</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">norm_type</span> <span class="o">=</span> <span class="n">norm_type</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">scale_grad_by_freq</span> <span class="o">=</span> <span class="n">scale_grad_by_freq</span>
        <span class="k">if</span> <span class="n">_weight</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">weight</span> <span class="o">=</span> <span class="n">Parameter</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">(</span><span class="n">num_embeddings</span><span class="p">,</span> <span class="n">embedding_dim</span><span class="p">))</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">reset_parameters</span><span class="p">()</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="k">assert</span> <span class="nb">list</span><span class="p">(</span><span class="n">_weight</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> <span class="o">==</span> <span class="p">[</span><span class="n">num_embeddings</span><span class="p">,</span> <span class="n">embedding_dim</span><span class="p">],</span> \
                <span class="s1">&#39;Shape of weight does not match num_embeddings and embedding_dim&#39;</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">weight</span> <span class="o">=</span> <span class="n">Parameter</span><span class="p">(</span><span class="n">_weight</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">sparse</span> <span class="o">=</span> <span class="n">sparse</span>

    <span class="k">def</span> <span class="nf">reset_parameters</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="n">init</span><span class="o">.</span><span class="n">normal_</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">weight</span><span class="p">)</span>
        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">padding_idx</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
            <span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">no_grad</span><span class="p">():</span>
                <span class="bp">self</span><span class="o">.</span><span class="n">weight</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">padding_idx</span><span class="p">]</span><span class="o">.</span><span class="n">fill_</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>

    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="nb">input</span><span class="p">):</span>
        <span class="k">return</span> <span class="n">F</span><span class="o">.</span><span class="n">embedding</span><span class="p">(</span>
            <span class="nb">input</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">weight</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">padding_idx</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_norm</span><span class="p">,</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">norm_type</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">scale_grad_by_freq</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">sparse</span><span class="p">)</span>

    <span class="k">def</span> <span class="nf">extra_repr</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="n">s</span> <span class="o">=</span> <span class="s1">&#39;</span><span class="si">{num_embeddings}</span><span class="s1">, </span><span class="si">{embedding_dim}</span><span class="s1">&#39;</span>
        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">padding_idx</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
            <span class="n">s</span> <span class="o">+=</span> <span class="s1">&#39;, padding_idx=</span><span class="si">{padding_idx}</span><span class="s1">&#39;</span>
        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_norm</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
            <span class="n">s</span> <span class="o">+=</span> <span class="s1">&#39;, max_norm=</span><span class="si">{max_norm}</span><span class="s1">&#39;</span>
        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">norm_type</span> <span class="o">!=</span> <span class="mi">2</span><span class="p">:</span>
            <span class="n">s</span> <span class="o">+=</span> <span class="s1">&#39;, norm_type=</span><span class="si">{norm_type}</span><span class="s1">&#39;</span>
        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">scale_grad_by_freq</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">False</span><span class="p">:</span>
            <span class="n">s</span> <span class="o">+=</span> <span class="s1">&#39;, scale_grad_by_freq=</span><span class="si">{scale_grad_by_freq}</span><span class="s1">&#39;</span>
        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">sparse</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">False</span><span class="p">:</span>
            <span class="n">s</span> <span class="o">+=</span> <span class="s1">&#39;, sparse=True&#39;</span>
        <span class="k">return</span> <span class="n">s</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="o">**</span><span class="bp">self</span><span class="o">.</span><span class="vm">__dict__</span><span class="p">)</span>

<div class="viewcode-block" id="Embedding.from_pretrained"><a class="viewcode-back" href="../../../../nn.html#torch.nn.Embedding.from_pretrained">[docs]</a>    <span class="nd">@classmethod</span>
    <span class="k">def</span> <span class="nf">from_pretrained</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">embeddings</span><span class="p">,</span> <span class="n">freeze</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">padding_idx</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
                        <span class="n">max_norm</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">norm_type</span><span class="o">=</span><span class="mf">2.</span><span class="p">,</span> <span class="n">scale_grad_by_freq</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
                        <span class="n">sparse</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
        <span class="sa">r</span><span class="sd">&quot;&quot;&quot;Creates Embedding instance from given 2-dimensional FloatTensor.</span>

<span class="sd">        Args:</span>
<span class="sd">            embeddings (Tensor): FloatTensor containing weights for the Embedding.</span>
<span class="sd">                First dimension is being passed to Embedding as ``num_embeddings``, second as ``embedding_dim``.</span>
<span class="sd">            freeze (boolean, optional): If ``True``, the tensor does not get updated in the learning process.</span>
<span class="sd">                Equivalent to ``embedding.weight.requires_grad = False``. Default: ``True``</span>
<span class="sd">            padding_idx (int, optional): See module initialization documentation.</span>
<span class="sd">            max_norm (float, optional): See module initialization documentation.</span>
<span class="sd">            norm_type (float, optional): See module initialization documentation. Default ``2``.</span>
<span class="sd">            scale_grad_by_freq (boolean, optional): See module initialization documentation. Default ``False``.</span>
<span class="sd">            sparse (bool, optional): See module initialization documentation.</span>

<span class="sd">        Examples::</span>

<span class="sd">            &gt;&gt;&gt; # FloatTensor containing pretrained weights</span>
<span class="sd">            &gt;&gt;&gt; weight = torch.FloatTensor([[1, 2.3, 3], [4, 5.1, 6.3]])</span>
<span class="sd">            &gt;&gt;&gt; embedding = nn.Embedding.from_pretrained(weight)</span>
<span class="sd">            &gt;&gt;&gt; # Get embeddings for index 1</span>
<span class="sd">            &gt;&gt;&gt; input = torch.LongTensor([1])</span>
<span class="sd">            &gt;&gt;&gt; embedding(input)</span>
<span class="sd">            tensor([[ 4.0000,  5.1000,  6.3000]])</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="k">assert</span> <span class="n">embeddings</span><span class="o">.</span><span class="n">dim</span><span class="p">()</span> <span class="o">==</span> <span class="mi">2</span><span class="p">,</span> \
            <span class="s1">&#39;Embeddings parameter is expected to be 2-dimensional&#39;</span>
        <span class="n">rows</span><span class="p">,</span> <span class="n">cols</span> <span class="o">=</span> <span class="n">embeddings</span><span class="o">.</span><span class="n">shape</span>
        <span class="n">embedding</span> <span class="o">=</span> <span class="bp">cls</span><span class="p">(</span>
            <span class="n">num_embeddings</span><span class="o">=</span><span class="n">rows</span><span class="p">,</span>
            <span class="n">embedding_dim</span><span class="o">=</span><span class="n">cols</span><span class="p">,</span>
            <span class="n">_weight</span><span class="o">=</span><span class="n">embeddings</span><span class="p">,</span>
            <span class="n">padding_idx</span><span class="o">=</span><span class="n">padding_idx</span><span class="p">,</span>
            <span class="n">max_norm</span><span class="o">=</span><span class="n">max_norm</span><span class="p">,</span>
            <span class="n">norm_type</span><span class="o">=</span><span class="n">norm_type</span><span class="p">,</span>
            <span class="n">scale_grad_by_freq</span><span class="o">=</span><span class="n">scale_grad_by_freq</span><span class="p">,</span>
            <span class="n">sparse</span><span class="o">=</span><span class="n">sparse</span><span class="p">)</span>
        <span class="n">embedding</span><span class="o">.</span><span class="n">weight</span><span class="o">.</span><span class="n">requires_grad</span> <span class="o">=</span> <span class="ow">not</span> <span class="n">freeze</span>
        <span class="k">return</span> <span class="n">embedding</span></div></div>


<div class="viewcode-block" id="EmbeddingBag"><a class="viewcode-back" href="../../../../nn.html#torch.nn.EmbeddingBag">[docs]</a><span class="k">class</span> <span class="nc">EmbeddingBag</span><span class="p">(</span><span class="n">Module</span><span class="p">):</span>
    <span class="sa">r</span><span class="sd">&quot;&quot;&quot;Computes sums or means of &#39;bags&#39; of embeddings, without instantiating the</span>
<span class="sd">    intermediate embeddings.</span>

<span class="sd">    For bags of constant length and no :attr:`per_sample_weights`, this class</span>

<span class="sd">        * with ``mode=&quot;sum&quot;`` is equivalent to :class:`~torch.nn.Embedding` followed by ``torch.sum(dim=0)``,</span>
<span class="sd">        * with ``mode=&quot;mean&quot;`` is equivalent to :class:`~torch.nn.Embedding` followed by ``torch.mean(dim=0)``,</span>
<span class="sd">        * with ``mode=&quot;max&quot;`` is equivalent to :class:`~torch.nn.Embedding` followed by ``torch.max(dim=0)``.</span>

<span class="sd">    However, :class:`~torch.nn.EmbeddingBag` is much more time and memory efficient than using a chain of these</span>
<span class="sd">    operations.</span>

<span class="sd">    EmbeddingBag also supports per-sample weights as an argument to the forward</span>
<span class="sd">    pass. This scales the output of the Embedding before performing a weighted</span>
<span class="sd">    reduction as specified by ``mode``. If :attr:`per_sample_weights`` is passed, the</span>
<span class="sd">    only supported ``mode`` is ``&quot;sum&quot;``, which computes a weighted sum according to</span>
<span class="sd">    :attr:`per_sample_weights`.</span>

<span class="sd">    Args:</span>
<span class="sd">        num_embeddings (int): size of the dictionary of embeddings</span>
<span class="sd">        embedding_dim (int): the size of each embedding vector</span>
<span class="sd">        max_norm (float, optional): If given, each embedding vector with norm larger than :attr:`max_norm`</span>
<span class="sd">                                    is renormalized to have norm :attr:`max_norm`.</span>
<span class="sd">        norm_type (float, optional): The p of the p-norm to compute for the :attr:`max_norm` option. Default ``2``.</span>
<span class="sd">        scale_grad_by_freq (boolean, optional): if given, this will scale gradients by the inverse of frequency of</span>
<span class="sd">                                                the words in the mini-batch. Default ``False``.</span>
<span class="sd">                                                Note: this option is not supported when ``mode=&quot;max&quot;``.</span>
<span class="sd">        mode (string, optional): ``&quot;sum&quot;``, ``&quot;mean&quot;`` or ``&quot;max&quot;``. Specifies the way to reduce the bag.</span>
<span class="sd">                                 ``&quot;sum&quot;`` computes the weighted sum, taking :attr:`per_sample_weights`</span>
<span class="sd">                                 into consideration. ``&quot;mean&quot;`` computes the average of the values</span>
<span class="sd">                                 in the bag, ``&quot;max&quot;`` computes the max value over each bag.</span>
<span class="sd">                                 Default: ``&quot;mean&quot;``</span>
<span class="sd">        sparse (bool, optional): if ``True``, gradient w.r.t. :attr:`weight` matrix will be a sparse tensor. See</span>
<span class="sd">                                 Notes for more details regarding sparse gradients. Note: this option is not</span>
<span class="sd">                                 supported when ``mode=&quot;max&quot;``.</span>
<span class="sd">        include_last_offset (bool, optional): if ``True``, :attr:`offsets` has one additional element, where the last element</span>
<span class="sd">                                      is equivalent to the size of `indices`. This matches the CSR format. Note:</span>
<span class="sd">                                      this option is currently only supported when ``mode=&quot;sum&quot;``.</span>

<span class="sd">    Attributes:</span>
<span class="sd">        weight (Tensor): the learnable weights of the module of shape `(num_embeddings, embedding_dim)`</span>
<span class="sd">                         initialized from :math:`\mathcal{N}(0, 1)`.</span>

<span class="sd">    Inputs: :attr:`input` (LongTensor), :attr:`offsets` (LongTensor, optional), and</span>
<span class="sd">        :attr:`per_index_weights` (Tensor, optional)</span>

<span class="sd">        - If :attr:`input` is 2D of shape `(B, N)`,</span>

<span class="sd">          it will be treated as ``B`` bags (sequences) each of fixed length ``N``, and</span>
<span class="sd">          this will return ``B`` values aggregated in a way depending on the :attr:`mode`.</span>
<span class="sd">          :attr:`offsets` is ignored and required to be ``None`` in this case.</span>

<span class="sd">        - If :attr:`input` is 1D of shape `(N)`,</span>

<span class="sd">          it will be treated as a concatenation of multiple bags (sequences).</span>
<span class="sd">          :attr:`offsets` is required to be a 1D tensor containing the</span>
<span class="sd">          starting index positions of each bag in :attr:`input`. Therefore,</span>
<span class="sd">          for :attr:`offsets` of shape `(B)`, :attr:`input` will be viewed as</span>
<span class="sd">          having ``B`` bags. Empty bags (i.e., having 0-length) will have</span>
<span class="sd">          returned vectors filled by zeros.</span>

<span class="sd">        per_sample_weights (Tensor, optional): a tensor of float / double weights, or None</span>
<span class="sd">            to indicate all weights should be taken to be ``1``. If specified, :attr:`per_sample_weights`</span>
<span class="sd">            must have exactly the same shape as input and is treated as having the same</span>
<span class="sd">            :attr:`offsets`, if those are not ``None``. Only supported for ``mode=&#39;sum&#39;``.</span>


<span class="sd">    Output shape: `(B, embedding_dim)`</span>

<span class="sd">    Examples::</span>

<span class="sd">        &gt;&gt;&gt; # an Embedding module containing 10 tensors of size 3</span>
<span class="sd">        &gt;&gt;&gt; embedding_sum = nn.EmbeddingBag(10, 3, mode=&#39;sum&#39;)</span>
<span class="sd">        &gt;&gt;&gt; # a batch of 2 samples of 4 indices each</span>
<span class="sd">        &gt;&gt;&gt; input = torch.LongTensor([1,2,4,5,4,3,2,9])</span>
<span class="sd">        &gt;&gt;&gt; offsets = torch.LongTensor([0,4])</span>
<span class="sd">        &gt;&gt;&gt; embedding_sum(input, offsets)</span>
<span class="sd">        tensor([[-0.8861, -5.4350, -0.0523],</span>
<span class="sd">                [ 1.1306, -2.5798, -1.0044]])</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="n">__constants__</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;num_embeddings&#39;</span><span class="p">,</span> <span class="s1">&#39;embedding_dim&#39;</span><span class="p">,</span> <span class="s1">&#39;max_norm&#39;</span><span class="p">,</span> <span class="s1">&#39;norm_type&#39;</span><span class="p">,</span>
                     <span class="s1">&#39;scale_grad_by_freq&#39;</span><span class="p">,</span> <span class="s1">&#39;mode&#39;</span><span class="p">,</span> <span class="s1">&#39;sparse&#39;</span><span class="p">,</span> <span class="s1">&#39;include_last_offset&#39;</span><span class="p">]</span>

    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">num_embeddings</span><span class="p">,</span> <span class="n">embedding_dim</span><span class="p">,</span>
                 <span class="n">max_norm</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">norm_type</span><span class="o">=</span><span class="mf">2.</span><span class="p">,</span> <span class="n">scale_grad_by_freq</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
                 <span class="n">mode</span><span class="o">=</span><span class="s1">&#39;mean&#39;</span><span class="p">,</span> <span class="n">sparse</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">_weight</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">include_last_offset</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
        <span class="nb">super</span><span class="p">(</span><span class="n">EmbeddingBag</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">num_embeddings</span> <span class="o">=</span> <span class="n">num_embeddings</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">embedding_dim</span> <span class="o">=</span> <span class="n">embedding_dim</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">max_norm</span> <span class="o">=</span> <span class="n">max_norm</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">norm_type</span> <span class="o">=</span> <span class="n">norm_type</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">scale_grad_by_freq</span> <span class="o">=</span> <span class="n">scale_grad_by_freq</span>
        <span class="k">if</span> <span class="n">_weight</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">weight</span> <span class="o">=</span> <span class="n">Parameter</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">(</span><span class="n">num_embeddings</span><span class="p">,</span> <span class="n">embedding_dim</span><span class="p">))</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">reset_parameters</span><span class="p">()</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="k">assert</span> <span class="nb">list</span><span class="p">(</span><span class="n">_weight</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> <span class="o">==</span> <span class="p">[</span><span class="n">num_embeddings</span><span class="p">,</span> <span class="n">embedding_dim</span><span class="p">],</span> \
                <span class="s1">&#39;Shape of weight does not match num_embeddings and embedding_dim&#39;</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">weight</span> <span class="o">=</span> <span class="n">Parameter</span><span class="p">(</span><span class="n">_weight</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">mode</span> <span class="o">=</span> <span class="n">mode</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">sparse</span> <span class="o">=</span> <span class="n">sparse</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">include_last_offset</span> <span class="o">=</span> <span class="n">include_last_offset</span>

    <span class="k">def</span> <span class="nf">reset_parameters</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="n">init</span><span class="o">.</span><span class="n">normal_</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">weight</span><span class="p">)</span>

    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="nb">input</span><span class="p">,</span> <span class="n">offsets</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">per_sample_weights</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
        <span class="c1"># type: (Tensor, Optional[Tensor], Optional[Tensor]) -&gt; Tensor</span>
        <span class="k">return</span> <span class="n">F</span><span class="o">.</span><span class="n">embedding_bag</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">weight</span><span class="p">,</span> <span class="n">offsets</span><span class="p">,</span>
                               <span class="bp">self</span><span class="o">.</span><span class="n">max_norm</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">norm_type</span><span class="p">,</span>
                               <span class="bp">self</span><span class="o">.</span><span class="n">scale_grad_by_freq</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">mode</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">sparse</span><span class="p">,</span>
                               <span class="n">per_sample_weights</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">include_last_offset</span><span class="p">)</span>

    <span class="k">def</span> <span class="nf">extra_repr</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="n">s</span> <span class="o">=</span> <span class="s1">&#39;</span><span class="si">{num_embeddings}</span><span class="s1">, </span><span class="si">{embedding_dim}</span><span class="s1">&#39;</span>
        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_norm</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
            <span class="n">s</span> <span class="o">+=</span> <span class="s1">&#39;, max_norm=</span><span class="si">{max_norm}</span><span class="s1">&#39;</span>
        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">norm_type</span> <span class="o">!=</span> <span class="mi">2</span><span class="p">:</span>
            <span class="n">s</span> <span class="o">+=</span> <span class="s1">&#39;, norm_type=</span><span class="si">{norm_type}</span><span class="s1">&#39;</span>
        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">scale_grad_by_freq</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">False</span><span class="p">:</span>
            <span class="n">s</span> <span class="o">+=</span> <span class="s1">&#39;, scale_grad_by_freq=</span><span class="si">{scale_grad_by_freq}</span><span class="s1">&#39;</span>
        <span class="n">s</span> <span class="o">+=</span> <span class="s1">&#39;, mode=</span><span class="si">{mode}</span><span class="s1">&#39;</span>
        <span class="k">return</span> <span class="n">s</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="o">**</span><span class="bp">self</span><span class="o">.</span><span class="vm">__dict__</span><span class="p">)</span>

<div class="viewcode-block" id="EmbeddingBag.from_pretrained"><a class="viewcode-back" href="../../../../nn.html#torch.nn.EmbeddingBag.from_pretrained">[docs]</a>    <span class="nd">@classmethod</span>
    <span class="k">def</span> <span class="nf">from_pretrained</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">embeddings</span><span class="p">,</span> <span class="n">freeze</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">max_norm</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
                        <span class="n">norm_type</span><span class="o">=</span><span class="mf">2.</span><span class="p">,</span> <span class="n">scale_grad_by_freq</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
                        <span class="n">mode</span><span class="o">=</span><span class="s1">&#39;mean&#39;</span><span class="p">,</span> <span class="n">sparse</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">include_last_offset</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
        <span class="sa">r</span><span class="sd">&quot;&quot;&quot;Creates EmbeddingBag instance from given 2-dimensional FloatTensor.</span>

<span class="sd">        Args:</span>
<span class="sd">            embeddings (Tensor): FloatTensor containing weights for the EmbeddingBag.</span>
<span class="sd">                First dimension is being passed to EmbeddingBag as &#39;num_embeddings&#39;, second as &#39;embedding_dim&#39;.</span>
<span class="sd">            freeze (boolean, optional): If ``True``, the tensor does not get updated in the learning process.</span>
<span class="sd">                Equivalent to ``embeddingbag.weight.requires_grad = False``. Default: ``True``</span>
<span class="sd">            max_norm (float, optional): See module initialization documentation. Default: ``None``</span>
<span class="sd">            norm_type (float, optional): See module initialization documentation. Default ``2``.</span>
<span class="sd">            scale_grad_by_freq (boolean, optional): See module initialization documentation. Default ``False``.</span>
<span class="sd">            mode (string, optional): See module initialization documentation. Default: ``&quot;mean&quot;``</span>
<span class="sd">            sparse (bool, optional): See module initialization documentation. Default: ``False``.</span>
<span class="sd">            include_last_offset (bool, optional): See module initialization documentation. Default: ``False``.</span>

<span class="sd">        Examples::</span>

<span class="sd">            &gt;&gt;&gt; # FloatTensor containing pretrained weights</span>
<span class="sd">            &gt;&gt;&gt; weight = torch.FloatTensor([[1, 2.3, 3], [4, 5.1, 6.3]])</span>
<span class="sd">            &gt;&gt;&gt; embeddingbag = nn.EmbeddingBag.from_pretrained(weight)</span>
<span class="sd">            &gt;&gt;&gt; # Get embeddings for index 1</span>
<span class="sd">            &gt;&gt;&gt; input = torch.LongTensor([[1, 0]])</span>
<span class="sd">            &gt;&gt;&gt; embeddingbag(input)</span>
<span class="sd">            tensor([[ 2.5000,  3.7000,  4.6500]])</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="k">assert</span> <span class="n">embeddings</span><span class="o">.</span><span class="n">dim</span><span class="p">()</span> <span class="o">==</span> <span class="mi">2</span><span class="p">,</span> \
            <span class="s1">&#39;Embeddings parameter is expected to be 2-dimensional&#39;</span>
        <span class="n">rows</span><span class="p">,</span> <span class="n">cols</span> <span class="o">=</span> <span class="n">embeddings</span><span class="o">.</span><span class="n">shape</span>
        <span class="n">embeddingbag</span> <span class="o">=</span> <span class="bp">cls</span><span class="p">(</span>
            <span class="n">num_embeddings</span><span class="o">=</span><span class="n">rows</span><span class="p">,</span>
            <span class="n">embedding_dim</span><span class="o">=</span><span class="n">cols</span><span class="p">,</span>
            <span class="n">_weight</span><span class="o">=</span><span class="n">embeddings</span><span class="p">,</span>
            <span class="n">max_norm</span><span class="o">=</span><span class="n">max_norm</span><span class="p">,</span>
            <span class="n">norm_type</span><span class="o">=</span><span class="n">norm_type</span><span class="p">,</span>
            <span class="n">scale_grad_by_freq</span><span class="o">=</span><span class="n">scale_grad_by_freq</span><span class="p">,</span>
            <span class="n">mode</span><span class="o">=</span><span class="n">mode</span><span class="p">,</span>
            <span class="n">sparse</span><span class="o">=</span><span class="n">sparse</span><span class="p">,</span>
            <span class="n">include_last_offset</span><span class="o">=</span><span class="n">include_last_offset</span><span class="p">)</span>
        <span class="n">embeddingbag</span><span class="o">.</span><span class="n">weight</span><span class="o">.</span><span class="n">requires_grad</span> <span class="o">=</span> <span class="ow">not</span> <span class="n">freeze</span>
        <span class="k">return</span> <span class="n">embeddingbag</span></div></div>
</pre></div>

             </article>
             
            </div>
            <footer>
  

  

    <hr>

  

  <div role="contentinfo">
    <p>
        &copy; Copyright 2019, Torch Contributors.

    </p>
  </div>
    
      <div>
        Built with <a href="http://sphinx-doc.org/">Sphinx</a> using a <a href="https://github.com/rtfd/sphinx_rtd_theme">theme</a> provided by <a href="https://readthedocs.org">Read the Docs</a>.
      </div>
     

</footer>

          </div>
        </div>

        <div class="pytorch-content-right" id="pytorch-content-right">
          <div class="pytorch-right-menu" id="pytorch-right-menu">
            <div class="pytorch-side-scroll" id="pytorch-side-scroll-right">
              
            </div>
          </div>
        </div>
      </section>
    </div>

  


  

     
       <script type="text/javascript" id="documentation_options" data-url_root="../../../../" src="../../../../_static/documentation_options.js"></script>
         <script src="../../../../_static/jquery.js"></script>
         <script src="../../../../_static/underscore.js"></script>
         <script src="../../../../_static/doctools.js"></script>
         <script src="../../../../_static/language_data.js"></script>
     

  

  <script type="text/javascript" src="../../../../_static/js/vendor/popper.min.js"></script>
  <script type="text/javascript" src="../../../../_static/js/vendor/bootstrap.min.js"></script>
  <script src="https://cdnjs.cloudflare.com/ajax/libs/list.js/1.5.0/list.min.js"></script>
  <script type="text/javascript" src="../../../../_static/js/theme.js"></script>

  <script type="text/javascript">
      jQuery(function () {
          SphinxRtdTheme.Navigation.enable(true);
      });
  </script>
 
<script>
  (function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||function(){
  (i[r].q=i[r].q||[]).push(arguments)},i[r].l=1*new Date();a=s.createElement(o),
  m=s.getElementsByTagName(o)[0];a.async=1;a.src=g;m.parentNode.insertBefore(a,m)
  })(window,document,'script','https://www.google-analytics.com/analytics.js','ga');

  ga('create', 'UA-90545585-1', 'auto');
  ga('send', 'pageview');

</script>

<script async src="https://www.googletagmanager.com/gtag/js?id=UA-117752657-2"></script>

<script>
  window.dataLayer = window.dataLayer || [];

  function gtag(){dataLayer.push(arguments);}

  gtag('js', new Date());
  gtag('config', 'UA-117752657-2');
</script>

<img height="1" width="1" style="border-style:none;" alt="" src="https://www.googleadservices.com/pagead/conversion/795629140/?label=txkmCPmdtosBENSssfsC&amp;guid=ON&amp;script=0"/>


  <!-- Begin Footer -->

  <div class="container-fluid docs-tutorials-resources" id="docs-tutorials-resources">
    <div class="container">
      <div class="row">
        <div class="col-md-4 text-center">
          <h2>Docs</h2>
          <p>Access comprehensive developer documentation for PyTorch</p>
          <a class="with-right-arrow" href="https://pytorch.org/docs/stable/index.html">View Docs</a>
        </div>

        <div class="col-md-4 text-center">
          <h2>Tutorials</h2>
          <p>Get in-depth tutorials for beginners and advanced developers</p>
          <a class="with-right-arrow" href="https://pytorch.org/tutorials">View Tutorials</a>
        </div>

        <div class="col-md-4 text-center">
          <h2>Resources</h2>
          <p>Find development resources and get your questions answered</p>
          <a class="with-right-arrow" href="https://pytorch.org/resources">View Resources</a>
        </div>
      </div>
    </div>
  </div>

  <footer class="site-footer">
    <div class="container footer-container">
      <div class="footer-logo-wrapper">
        <a href="https://pytorch.org/" class="footer-logo"></a>
      </div>

      <div class="footer-links-wrapper">
        <div class="footer-links-col">
          <ul>
            <li class="list-title"><a href="https://pytorch.org/">PyTorch</a></li>
            <li><a href="https://pytorch.org/get-started">Get Started</a></li>
            <li><a href="https://pytorch.org/features">Features</a></li>
            <li><a href="https://pytorch.org/ecosystem">Ecosystem</a></li>
            <li><a href="https://pytorch.org/blog/">Blog</a></li>
            <li><a href="https://github.com/pytorch/pytorch/blob/master/CONTRIBUTING.md">Contributing</a></li>
          </ul>
        </div>

        <div class="footer-links-col">
          <ul>
            <li class="list-title"><a href="https://pytorch.org/resources">Resources</a></li>
            <li><a href="https://pytorch.org/tutorials">Tutorials</a></li>
            <li><a href="https://pytorch.org/docs/stable/index.html">Docs</a></li>
            <li><a href="https://discuss.pytorch.org" target="_blank">Discuss</a></li>
            <li><a href="https://github.com/pytorch/pytorch/issues" target="_blank">Github Issues</a></li>
            <li><a href="https://pytorch.org/assets/brand-guidelines/PyTorch-Brand-Guidelines.pdf" target="_blank">Brand Guidelines</a></li>
          </ul>
        </div>

        <div class="footer-links-col follow-us-col">
          <ul>
            <li class="list-title">Stay Connected</li>
            <li>
              <div id="mc_embed_signup">
                <form
                  action="https://twitter.us14.list-manage.com/subscribe/post?u=75419c71fe0a935e53dfa4a3f&id=91d0dccd39"
                  method="post"
                  id="mc-embedded-subscribe-form"
                  name="mc-embedded-subscribe-form"
                  class="email-subscribe-form validate"
                  target="_blank"
                  novalidate>
                  <div id="mc_embed_signup_scroll" class="email-subscribe-form-fields-wrapper">
                    <div class="mc-field-group">
                      <label for="mce-EMAIL" style="display:none;">Email Address</label>
                      <input type="email" value="" name="EMAIL" class="required email" id="mce-EMAIL" placeholder="Email Address">
                    </div>

                    <div id="mce-responses" class="clear">
                      <div class="response" id="mce-error-response" style="display:none"></div>
                      <div class="response" id="mce-success-response" style="display:none"></div>
                    </div>    <!-- real people should not fill this in and expect good things - do not remove this or risk form bot signups-->

                    <div style="position: absolute; left: -5000px;" aria-hidden="true"><input type="text" name="b_75419c71fe0a935e53dfa4a3f_91d0dccd39" tabindex="-1" value=""></div>

                    <div class="clear">
                      <input type="submit" value="" name="subscribe" id="mc-embedded-subscribe" class="button email-subscribe-button">
                    </div>
                  </div>
                </form>
              </div>

            </li>
          </ul>

          <div class="footer-social-icons">
            <a href="https://www.facebook.com/pytorch" target="_blank" class="facebook"></a>
            <a href="https://twitter.com/pytorch" target="_blank" class="twitter"></a>
            <a href="https://www.youtube.com/pytorch" target="_blank" class="youtube"></a>
          </div>
        </div>
      </div>
    </div>
  </footer>

  <div class="cookie-banner-wrapper">
  <div class="container">
    <p class="gdpr-notice">To analyze traffic and optimize your experience, we serve cookies on this site. By clicking or navigating, you agree to allow our usage of cookies. As the current maintainers of this site, Facebook’s Cookies Policy applies. Learn more, including about available controls: <a href="https://www.facebook.com/policies/cookies/">Cookies Policy</a>.</p>
    <img class="close-button" src="../../../../_static/images/pytorch-x.svg">
  </div>
</div>

  <!-- End Footer -->

  <!-- Begin Mobile Menu -->

  <div class="mobile-main-menu">
    <div class="container-fluid">
      <div class="container">
        <div class="mobile-main-menu-header-container">
          <a class="header-logo" href="https://pytorch.org/" aria-label="PyTorch"></a>
          <a class="main-menu-close-button" href="#" data-behavior="close-mobile-menu"></a>
        </div>
      </div>
    </div>

    <div class="mobile-main-menu-links-container">
      <div class="main-menu">
        <ul>
          <li>
            <a href="https://pytorch.org/get-started">Get Started</a>
          </li>

          <li>
            <a href="https://pytorch.org/features">Features</a>
          </li>

          <li>
            <a href="https://pytorch.org/ecosystem">Ecosystem</a>
          </li>

          <li>
            <a href="https://pytorch.org/mobile">Mobile</a>
          </li>

          <li>
            <a href="https://pytorch.org/hub">PyTorch Hub</a>
          </li>

          <li>
            <a href="https://pytorch.org/blog/">Blog</a>
          </li>

          <li>
            <a href="https://pytorch.org/tutorials">Tutorials</a>
          </li>

          <li class="active">
            <a href="https://pytorch.org/docs/stable/index.html">Docs</a>
          </li>

          <li>
            <a href="https://pytorch.org/resources">Resources</a>
          </li>

          <li>
            <a href="https://github.com/pytorch/pytorch">Github</a>
          </li>
        </ul>
      </div>
    </div>
  </div>

  <!-- End Mobile Menu -->

  <script type="text/javascript" src="../../../../_static/js/vendor/anchor.min.js"></script>

  <script type="text/javascript">
    $(document).ready(function() {
      mobileMenu.bind();
      mobileTOC.bind();
      pytorchAnchors.bind();
      sideMenus.bind();
      scrollToAnchor.bind();
      highlightNavigation.bind();
      mainMenuDropdown.bind();
      filterTags.bind();

      // Remove any empty p tags that Sphinx adds
      $("[data-tags='null']").remove();

      // Add class to links that have code blocks, since we cannot create links in code blocks
      $("article.pytorch-article a span.pre").each(function(e) {
        $(this).closest("a").addClass("has-code");
      });
    })
  </script>
</body>
</html>